1. Technical Field
The present disclosure relates to medical imaging of the heart, and more particularly, to detection of the left ventricular blood pool in the heart.
2. Discussion of Related Art
Cardiovascular disease has become the largest cause of death in the modern world and is an important health concern. Imaging technologies such as magnetic resonance (MR) imaging allow physicians to non-invasively observe the behavior of the heart. Physicians are particularly interested in the left ventricle (LV) because it pumps oxygenated blood out to the rest of the body. To diagnose medical conditions, it is useful to be able to quantify the volume of blood pool of the LV over time and estimate its ejection fraction, cardiac output, peak ejection rate, filling rate, myocardial thickening, etc. These quantities can be computed once the LV is outlined in several images of the heart. Manual outlining of the LV is very cumbersome however, and many physicians can only manually outline the end-diastolic (ED) and end-systolic (ES) phases. While the ejection fraction and cardiac output can be computed from the resulting outlined LVs, they do not provide enough information to estimate peak ejection rate or filling rate.
Accordingly, it would be beneficial to be able to provide a system that automatically segments images of the heart to locate the LV. As a first step it is important to localize the LV blood pool. Then, the localized blood pool can be used to initialize more elaborate LV segmentation methods. However, it can be difficult to localize the LV blood pool because MR intensities are not consistent across acquisitions and blood pixels cannot easily be identified in the images. Further, many acquisitions cover slices beyond the LV itself to guarantee it is seen in all phases. This means that some slices can be below the apex and contain no blood pool, and some slices can be above the mitral valve and contain the left atrium blood pool.
Some researchers have constructed models to aid in LV segmentation. One conventional method uses a 4D probabilistic atlas of the heart and a 3D intensity template to register an ED frame to localize the left and right ventricles. A second conventional method uses a hybrid active shape and appearance model to locate the heart using a Hough transform. However, both of these methods are too slow for clinical practice. Further, models have difficulty capturing variability outside their training sets. For example, pathological cases that fall outside the standard set of shapes may not be recognized and appearance models may need to be re-trained for new acquisition protocols and sequences.
One conventional modeling method is fast enough for clinical practice, but still depends on a learned appearance represented by a Markov chain. Another conventional modeling method combines a statistical model with coupled mesh surfaces. However, many of the datasets tested using the statistical modeling method exhibit breathing artifacts and through-plane motion. Further, the statistical modeling method assumes that the heart is located in the center of an image, which is rarely a valid assumption.
Other conventional methods use simple image processing techniques to aid in LV segmentation. One conventional image processing method operates on the assumption that the coverage of a short-axis image stack stops at the mitral valve and does not go into the atrium. However, this assumption is not reasonable since physicians tend to increase the coverage of the image stack to correct for potential motion after the acquisition of the localized images to make sure that the LV is visible during all phases. Further, when the top slice extends into the atrium, the method can not separate the LV from the right ventricle (RV) without user intervention at the mitral valve.
Thus, there is a need for more efficient methods of automatically detecting the LV blood pool.